Flipboard

MARCH 3, 2023

Data proliferation has become a norm and as organizations become more data driven, automating data pipelines that enable data ingestion, curation, …

Data Ingestion 123

Data Ingestion ML Automation Big Data 123

Pickl AI

MARCH 13, 2025

RabbitMQ ensures reliable, structured message delivery, while Kafka excels in real-time, high-volume data streaming. Choosing between them depends on your systems needsRabbitMQ is best for workflows, while Kafka is ideal for event-driven architectures and big data processing. Thats where message brokers come in.

Big Data 52

Big Data Data Ingestion AI AI 52

Marktechpost

APRIL 1, 2024

Existing research emphasizes the significance of distributed processing and data quality control for enhancing LLMs. Utilizing frameworks like Slurm and Spark enables efficient big data management, while data quality improvements through deduplication, decontamination, and sentence length adjustments refine training datasets.

Large Language Models 113

Large Language Models ETL Data Ingestion Data Quality 113

IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 130

Data Quality Metadata ETL Big Data 130

Pickl AI

JULY 30, 2024

Summary: Apache NiFi is a powerful open-source data ingestion platform design to automate data flow management between systems. Its architecture includes FlowFiles, repositories, and processors, enabling efficient data processing and transformation.

Data Ingestion 52

Data Ingestion ETL Big Data Data Integration 52

AWS Machine Learning Blog

OCTOBER 2, 2024

Manage data through standard methods of data ingestion and use Enriching LLMs with new data is imperative for LLMs to provide more contextual answers without the need for extensive fine-tuning or the overhead of building a specific corporate LLM. Tanvi Singhal is a Data Scientist within AWS Professional Services.

Generative AI 124

Generative AI Data Ingestion AI AI 124

AWS Machine Learning Blog

DECEMBER 18, 2023

MongoDB Atlas offers automatic sharding, horizontal scalability, and flexible indexing for high-volume data ingestion. Among all, the native time series capabilities is a standout feature, making it ideal for a managing high volume of time-series data, such as business critical application data, telemetry, server logs and more.

Data Extraction 122

Data Extraction Data Ingestion ML Machine Learning 122

Pickl AI

JULY 6, 2023

Thus, making it easier for analysts and data scientists to leverage their SQL skills for Big Data analysis. It applies the data structure during querying rather than data ingestion. How Data Flows in Hive In Hive, data flows through several steps to enable querying and analysis.

Big Data 52

Big Data Data Analysis ETL Metadata 52

Pickl AI

AUGUST 1, 2023

The key sectors where Data Engineering has a major contribution include IT, Internet/eCommerce, and Banking & Insurance. Salary of a Data Engineer ranges between ₹ 3.1 Data Storage: Storing the collected data in various storage systems, such as relational databases, NoSQL databases, data lakes, or data warehouses.

Big Data 52

Big Data Data Analysis Data Scientist Data Science 52

AWS Machine Learning Blog

AUGUST 8, 2024

About the Authors Apurva Gawad is a Senior Data Engineer at Twilio specializing in building scalable systems for data ingestion and empowering business teams to derive valuable insights from data. She has a keen interest in AI exploration, blending technical expertise with a passion for innovation.

Metadata 123

Metadata LLM Prompt Engineer Prompt Engineering 123

Pickl AI

JUNE 29, 2023

It initiates the collection, indexing, and analysis of machine-generated data in real-time. It helps harness the power of big data and turn it into actionable intelligence. Moreover, it allows users to ingest data from different sources. Additionally, Splunk can process and index massive volumes of data.

Big Data 52

Big Data DevOps Data Analysis Machine Learning 52

AWS Machine Learning Blog

AUGUST 21, 2024

His knowledge ranges from application architecture to big data, analytics, and machine learning. Amazon SageMaker Canvas is a no-code machine learning (ML) service that empowers business analysts and domain experts to build, train, and deploy ML models without writing a single line of code.

Machine Learning 119

Machine Learning Data Scientist ML Data Quality 119

ODSC - Open Data Science

SEPTEMBER 29, 2023

But, the amount of data companies must manage is growing at a staggering rate. Research analyst firm Statista forecasts global data creation will hit 180 zettabytes by 2025. In our discussion, we cover the genesis of the HPCC Systems data lake platform and what makes it different from other big data solutions currently available.

Big Data 52

Big Data ETL Data Science Data Ingestion 52

Pickl AI

APRIL 6, 2023

Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists. How to Become an Azure Data Engineer? Answer : Polybase helps optimize data ingestion into PDW and supports T-SQL.

Big Data 52

Big Data ETL Data Ingestion Software Engineer 52

Pickl AI

OCTOBER 10, 2024

Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.

ETL 52

ETL Data Quality Machine Learning Business Intelligence 52

AWS Machine Learning Blog

OCTOBER 6, 2023

For ingestion, data can be updated in an offline mode, whereas inference needs to happen in milliseconds. He is deeply passionate about applying ML/DL and big data techniques to solve real-world problems. SageMaker Feature Store ensures that offline and online datasets remain in sync.

Generative AI 136

Generative AI LLM Natural Language Processing Metadata 136

The MLOps Blog

FEBRUARY 5, 2023

It can be used to perform complex data processing tasks such as windowed aggregations, joins, and event-time processing. Apache Spark : An open-source, distributed computing system that can handle big data processing tasks. Azure Stream Analytics : A cloud-based service that can be used to process streaming data in real-time.

Machine Learning 72

Machine Learning Big Data Auto-complete Data Ingestion 72

TransOrg Analytics

AUGUST 21, 2024

Such success stories have largely depended on Data Engineering processes. This article explores how data engineering can improve Customer 360 initiatives for AWS data engineering , big data engineering, and data analytics companies. What Are Customer 360 Initiatives?

Big Data Engineer 52

Big Data Engineer ETL Data Ingestion Data Integration 52

DataRobot Blog

SEPTEMBER 13, 2022

What Do Data Scientists Do? Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. What data scientists do is directly tied to an organization’s AI maturity level.

Data Scientist 52

Data Scientist Automation ML Machine Learning 52

Pickl AI

SEPTEMBER 5, 2024

Unified Data Services: Azure Synapse Analytics combines big data and data warehousing, offering a unified analytics experience. Azure’s global network of data centres ensures high availability and performance, making it a powerful platform for Data Scientists to leverage for diverse data-driven projects.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Analysis 52

Pickl AI

APRIL 14, 2023

In addition, it also defines the framework wherein it is decided what action needs to be taken on certain data. And so, a company dealing in Big Data Analysis needs to follow stringent Data Governance policies. Hence the significance of a well-defined governance strategy becomes fundamental for any organization.

Data Platform 52

Data Platform Data Integration Data Ingestion Automation 52

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

FEBRUARY 7, 2025

For options 2, 3, and 4, the SageMaker Projects Portfolio provides project templates to run ML experiment pipelines, steps including data ingestion, model training, and registering the model in the model registry. You can choose which option to use depending on your setup.

ML 63

ML Data Scientist ML Engineer Data Science 63

The MLOps Blog

MARCH 15, 2023

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process. Data Ingestion : Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.

ETL 59

ETL Categorization Data Integration Automation 59

AWS Machine Learning Blog

SEPTEMBER 11, 2024

SIMD describes computers with multiple processing elements that perform the same operation on multiple data points simultaneously. SIMT describes processors that are able to operate on data vectors and arrays (as opposed to just scalars), and therefore handle big data workloads efficiently.

ML 102

ML Deep Learning Algorithm Large Language Models 102

The MLOps Blog

AUGUST 11, 2023

1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., The next section delves into these architectural patterns, exploring how they are leveraged in machine learning pipelines to streamline data ingestion, processing, model training, and deployment.

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

AWS Machine Learning Blog

OCTOBER 23, 2024

Data flow Here is an example of this data flow for an Agent Creator pipeline that involves data ingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. He currently is working on Generative AI for data integration.

Generative AI 82

Generative AI IDP LLM Automation 82

AWS Machine Learning Blog

SEPTEMBER 18, 2024

Hosted on Amazon ECS with tasks run on Fargate, this platform streamlines the end-to-end ML workflow, from data ingestion to model deployment. An example direct acyclic graph (DAG) might automate data ingestion, processing, model training, and deployment tasks, ensuring that each step is run in the correct order and at the right time.

Machine Learning 119

Machine Learning Data Scientist ML Data Ingestion 119

TransOrg Analytics

SEPTEMBER 24, 2024

AWS Data Exchange: Access third-party datasets directly within AWS. Data & ML/LLM Ops on AWS Amazon SageMaker: Comprehensive ML service to build, train, and deploy models at scale. Amazon EMR: Managed big data service to process large datasets quickly.

ETL 52

ETL LLM Data Ingestion Automation 52

TransOrg Analytics

SEPTEMBER 24, 2024

AWS Data Exchange: Access third-party datasets directly within AWS. Data & ML/LLM Ops on AWS Amazon SageMaker: Comprehensive ML service to build, train, and deploy models at scale. Amazon EMR: Managed big data service to process large datasets quickly.

ETL 52

ETL LLM Data Ingestion Automation 52

AWS Machine Learning Blog

NOVEMBER 7, 2024

We explored multiple big data processing solutions and decided to use an Amazon SageMaker Processing job for the following reasons: It’s highly configurable, with support of pre-built images, custom cluster requirements, and containers. When inference data is ingested on Amazon S3, EventBridge automatically runs the inference pipeline.

ML 110

ML Machine Learning Automation Data Scientist 110

Pickl AI

DECEMBER 26, 2024

Summary: “Data Science in a Cloud World” highlights how cloud computing transforms Data Science by providing scalable, cost-effective solutions for big data, Machine Learning, and real-time analytics. This accessibility democratises Data Science, making it available to businesses of all sizes.

Data Science 52

Data Science Machine Learning Data Scientist Big Data 52